Basepoint estimator

ABSTRACT

A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

BACKGROUND OF THE INVENTION

This invention was made with government support under Contract No. N00019-02-C-3003 awarded by the United States Navy. The government may therefore have certain rights in this invention.

This application relates to control systems, and more particularly to a method of estimating a basepoint in a multivariable control system according to a relative goal prioritization scheme.

In control theory, a given control system may have a plurality of goals and a plurality of limits. Limits are inequality constraints on system dynamic variables. An example limit may be to prevent an engine temperature from exceeding a certain temperature to prevent engine deterioration. An example goal may be to achieve a certain engine thrust level, such as a thrust of 10,000 pounds. While it is desirable to achieve goals, it is necessary to meet limits.

A multivariable system may include a number of effectors that can be adjusted to meet system goals and limits. In some cases, a system may be cross-coupled, which means that each effector change may affect goals and limits with varying dynamics. In a cross-coupled system, it may not be possible to change a single effector in isolation to affect a single goal or limit, as a change in one effector may affect a plurality of goals or limits.

A basepoint is a set of system values corresponding to a state of equilibrium in which a rate of change for the system is zero, a predetermined amount of limits are met, and a quantity of goals are fulfilled.

SUMMARY OF THE INVENTION

A method of estimating a basepoint includes receiving a plurality of goals, wherein each goal has a desired value, and receiving a plurality of sensor feedback signals from a controlled system. A plurality of predicted output values of the controlled system are received from a mathematical model. A desired change for a plurality of basepoint values is estimated in response to the goals, the feedback, and the predicted output values. An actual change in basepoint values is calculated in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits. The actual change in basepoint values is combined with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.

These and other features of the present invention can be best understood from the following specification and drawings, the following of which is a brief description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an example multivariable control system.

FIG. 2 schematically illustrates a basepoint estimator that could be used in the multivariable control system of FIG. 1.

FIG. 3 schematically illustrates another basepoint estimator that could be used in the multivariable control system of FIG. 1.

FIG. 4 schematically illustrates a quadratic problem solver from the basepoint estimator of FIG. 3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 schematically illustrates an example multivariable control system 10. The system 10 includes a basepoint estimator 16 operable to receive a plurality of goals 12 (“yref”), a plurality of maximum limits 14 (“ymax”), a feedback signal 28 (“yfb”), and a predicted output 19 (“ŷ”) from a mathematical model 22, and is operable to calculate a basepoint estimate 20 in response to the goals 12, limits 14, feedback signal 28, and model predictions 19. It is understood that the feedback signal 28 could include a plurality of feedback signals. The model predictions 19 correspond to predicted behavior of a controlled system 32, include an associated entry for the feedback signal 28 (or if there are a plurality of feedback signals, include an associated entry for each of the plurality of feedback signals), and may include additional variables not associated with a feedback sensor. A basepoint is a set of values corresponding to an equilibrium point at which a rate of change of the controlled system 32 is zero, and at which a predetermined amount of limits 14 are met and a quantity of goals 12 are fulfilled according to a goal prioritization scheme.

One embodiment of a multivariable control system 10 is described in commonly-assigned, co-pending U.S. Appln. No. 12/115,574 (Attorney Docket No. 67097-908 PUS1), the disclosure of which is incorporated herein by reference.

The basepoint estimate 20 is transmitted to the mathematical model 22 and to control logic 24. In one example, the mathematical model 22 is a time varying linear model, such as a quasi-linear parameter varying (“QLPV”) model. The basepoint estimate 20 includes a change in an estimated state 42 (“Δ{circumflex over (x)}”) of the controlled system 32, internal reference values 44 (“yr”), free basepoint values 46 (“{circumflex over (b)}pf”) that do not have reference values, and basepoint values 48 (“ŷcb”) associated with variables having limits. Throughout this application a carat (“̂”) corresponds to an estimated value.

The control logic 24 receives the basepoint estimate 20 and produces actuator requests 30 (“urq”), which are transmitted to the controlled system 32 to effect change in the controlled system 32. In one example the controlled system 32 is a vehicle, such as an aircraft. However, it is understood that the multivariable control system 10 could be used to control other systems. The transmission of actuator requests 30 to the controlled system 32 may include transmitting the actuator requests 30 to actuators which control effectors that may be adjusted to control the controlled system 32. The feedback signal 28 is transmitted from the controlled system 32 back to the multivariable control system 10. The basepoint estimator 16, basepoint estimate 20, other control logic 24, actuator requests 30, QLPV model 22, and predicted output 19 (“ŷ”) collectively form an estimator loop.

The mathematical model 22 may be based on equations #1-2, as shown below:

x _(n+1) =A(p)*(x _(n) −xb _(n))+B(p)*(urq _(n) −ub _(n))+xb _(n)  equation #1

y _(n) =C(p)*(x _(n) −xb _(n))+D(p)*(urq _(n) −ub _(n))+yb _(nn)  equation #2

where xb_(n), ub_(n), and yb_(nn) are basepoint values;

x corresponds to the state of the controlled system 32;

y_(n) corresponds to outputs of the controlled system 32;

urq_(n) corresponds to actuator requests;

p corresponds to parameters; and

A, B, C, and D are coefficients.

In the basepoint estimator 16, the collection of basepoints in the vectors xb, ub, and yb are regrouped into vectors yr, bpf, and ycb, where yr corresponds to basepoints for variables for which there are associated desired values (or “goals”), ycb corresponds to basepoints for variables for which there are limits, and bpf is a vector containing the remaining basepoints (or “free basepoints”).

FIG. 2 schematically illustrates a basepoint estimator 16 a that could be used in the multivariable control system 10 of FIG. 1. The basepoint estimator 16 a is simplified in that it does not hold limits. The basepoint estimator 16 a is operable to calculate a model prediction error 50 (“{tilde over (y)}”) in response to the feedback signal 28 and the model predictions 19. An estimator module 60 calculates the change in the estimated system state 42, a desired change in free basepoint values 62 (“Δ{circumflex over (b)}pf 1”), and a desired change in basepoint values associated with variables having limits 64 (“Δŷcb1”) in response to the model prediction error 50. The desired changes 62, 64 are combined to form a desired change in basepoint values 66 (“x_bpe”) vector.

Identity matrix 68 a sets an actual change in basepoint values 70 (“x2”) equal to the desired change in basepoint values 66. Identity matrix 68 b sets reference values 44 equal to goals 12. The identity matrices 68 a and 68 b are used because FIG. 2 illustrates a simplified basepoint estimator 16. It is understood that identity matrices may not be able to be used in certain cases, such as when limits are active in the controlled system 32.

The actual change in basepoint values 70 is processed with a priori basepoint values 46 a (“ bpf”) and 48 a (“ ycb”) to estimate basepoint values 46 (“{circumflex over (b)}pf”) and 48 (“ŷcb”). Throughout this application, a line above a variable indicates an a priori estimate of that variable. For example, “ x” would correspond to an a priori value of x. A processing step 72 a (“z⁻¹”) indicates that a value from a previous iteration, or a “last pass value”, is used to produce a new value. One of ordinary skill in the art would understand how to perform such a processing step.

FIG. 3 schematically illustrates a basepoint estimator 16 b that is operable to handle active limits. A quadratic programming (“QP”) solver module 74 is used to calculate the actual change in basepoint values 70 (“x2”) in response to the desired change in basepoint values 66, a mathematical model matrix 76 (“CE”), a mathematical model matrix 78 (“J”), and processed limits 80 (“K”). The matrices 76 and 78 may be calculated according to equations 3-10 shown below.

$\begin{matrix} {{CE} = \begin{bmatrix} I & 0 & 0 \\ {{CE}\; 1} & {{CE}\; 2} & {{CE}\; 3} \end{bmatrix}} & {{equation}\mspace{14mu} \# \; 3} \\ {{CE} \equiv \frac{\partial\hat{y}}{{\partial y}\; r}} & {{equation}\mspace{14mu} \# \; 4} \\ {{{CE}\; 2} \equiv \frac{\partial\hat{y}}{\partial{bpf}}} & {{equation}\mspace{14mu} \# \; 5} \\ {{{CE}\; 3} \equiv \frac{\partial\hat{y}}{\partial{ycb}}} & {{equation}\mspace{14mu} \# \; 6} \\ {J = \left\lbrack {J\; 1\mspace{14mu} J\; 2\mspace{14mu} J\; 3} \right\rbrack} & {{equation}\mspace{14mu} \# \; 7} \\ {{J\; 1} \equiv {{\frac{{\partial\hat{y}}c}{\partial\hat{y}} \cdot {CE}}\; 1}} & {{equation}\mspace{14mu} \# \; 8} \\ {{J\; 2} \equiv {{\frac{{\partial\hat{y}}c}{\partial\hat{y}} \cdot {CE}}\; 2}} & {{equation}\mspace{14mu} \# \; 9} \\ {{J\; 3} \equiv {{\frac{{\partial\hat{y}}c}{\partial\hat{y}} \cdot {CE}}\; 3}} & {{equation}\mspace{14mu} \# \; 10} \end{matrix}$

where ŷc corresponds to a portion of 9 that has corresponding limited variables.

The basepoint estimator 16 b is operable to calculate a difference between the goals 12 and a last pass value (see processing step 72 b) of reference values 44 to determine nominal goal changes 82 (“Δyr_nom”). The nominal goal changes 82 are included in the desired change in basepoint values 66. A first gain matrix 84 (“gref”) may be used to provide a smooth transition off a limit. For example, if a limit was active, and was then found to be inactive, the gain matrix 84 could provide a smooth transition off the limit so that the controlled system 32 does not experience an abrupt change.

The basepoint estimator 16 b is also operable to calculate a difference between the plurality of limits 14 and a last pass value (see processing step 72 c) of basepoint values 48 (“ŷcb”) to determine a remaining data range 86 (“yc_(available)”) until a limit is active. Those with ordinary skill in the art could also replace ŷcb, the anticipated value of the limited variable at equilibrium in signal 48, with ŷc, the prediction of the dynamically changing value of the limited variable from the mathematical model 22. A second gain matrix 88 (“glim”) may be applied to the remaining data range 86 to produce processed limits 80 (“K”). The second gain matrix 88 can facilitate a smooth transition onto a limit, so that the controlled system 32 system may asymptotically reach a limit and not abruptly hit the limit.

In the example of FIG. 3, actual change in basepoint values 70 (“x2”) is a vector that includes actual changes in perturbations to yr, bpf, and ycb. These perturbations are added to the a priori basepoint values 46 a (“ bpf”) and 48 a (“ ycb”) and to goal reference values 12 (“yref”) to form updated values for internal reference vales 44 (“yr”), and basepoint values 46 (“{circumflex over (b)}pf”) and 48 (“ŷcb”).

FIG. 4 schematically illustrates the QP solver 74 of FIG. 3 in greater detail. FIG. 4 is an example of how one might solve the QP problem active set algorithm using a big-K approach to tolerating infeasible starting points. If a limit or a plurality of limits are active, the multivariable control system 10 may change the behavior of the controlled system 32 to avoid exceeding the active limits. To achieve this, the QP solver module 74 chooses the actual change in basepoint values 70 (“x2”) to minimize a performance index (“PI”), represented by equation #11 below, subject to the constraints shown in equations #12-14 below. Equations #15-16 indicate a notation that may be used for the PI and the constraints.

$\begin{matrix} {{PI} = {{\frac{1}{2}{\left( {{\Delta \; {yr}} - {\Delta \; {yr\_ nom}}} \right)_{n + 1}}_{Q}^{2}} + {\frac{1}{2}{{x\; 2_{n}}}_{R}^{2}}}} & {{equation}\mspace{14mu} \# \; 11} \end{matrix}$

where Q corresponds to goal-weighting matrix 96; and

R corresponds to x2-weighting matrix 97.

J·x2≦K≡g lim·(Y max−ŷcb _(n))  equation #12

Δyr ₁₃ nom=gref·(yref−yr)_(n) +Δyref _(n+1)  equation #13

[C1 C2 C3]·x2=[C1 C2 C3]·x _(—) bpe  equation #14

∥x∥ _(Q) ² =x ^(T) Q·x  equation #15

Δ( )_(n+1)≡( )_(n+1)−( )_(n)  equation #16

The goal-weighting matrix 96 (“Q”) is a positive semi-definite symmetric matrix, which includes weights on enabled goals, with higher weights corresponding to higher priority goals. The elements of Δyr_nom and Δyr are elements of the larger vectors x_bpe and x2, respectively. The x2-weighting matrix 97 (“R”) is a positive definite symmetric matrix and corresponds to weights on x2. The weight on x2 is optional, but may be used to reduce sensitivity to model errors in CE and in the mathematical model 22.

The optimization problem of equation #11 can be related to basepoint estimator requirements by considering how the problem simplifies when gref=I and glim=I, where “I” is an identity matrix of dimensions determined by context. This may be only explanatory in that if the gain matrices 84 (“gref”) and 88 (“glim”) have these values, a change in reference values 44 (“yr”) and free basepoint values 46 (“bpf”) may be abrupt when limits switch between being active and inactive.

In this simplified case, the optimization problem of equation #11 may be minimized by #17, subject to the constraints of equations #18-20, as shown below.

$\begin{matrix} {{PI} = {\frac{1}{2}{\left( {{y\; r} - {yref}} \right)_{n + 1}}_{Q}^{2}}} & {{equation}\mspace{14mu} \# \; 17} \end{matrix}$

ŷcb_(n+1)≦Y max  equation #18

ŷcb _(n+1) =ŷcb _(n) +J·x2+Δ ycb _(n+1)  equation #19

yr _(n+1) =yr _(n) +[I 0 0]·x2  equation #20

This is explanatory in that equations #17-19 are a mathematical expression of the basepoint estimator 16 requirement of making the internal reference value 44 (“yr”) as close to yref as possible (equation #17) while meeting a predetermined quantity of limits (equation #18). In one example, the predetermined quantity of limits corresponds to all received and enabled limits. This basepoint estimator design varies x2 to achieve this requirement, and equations #19-20 indicate how x2 variations affect the limits and goals, respectively.

Using values between “0” and “1” for the gain matrices 84, 88 allows a control system designer to relax this basepoint estimator 16 requirement of equations 17-18 for brief transients as limits change between being active and inactive. This enables the basepoint estimator 16 to be less sensitive to model errors, and to provide a smoother response.

Equations 11 through 14 specify a QP problem, which can be solved using a variety of known methods and software. Equations 11 through 14 can be put into a standard QP form as follows:

$\begin{matrix} {{PI} = {{\frac{1}{2}x\; 2^{T}{H \cdot x}\; 2} - {x\; 2^{T}f}}} & {{equation}\mspace{14mu} \# \; 21} \end{matrix}$

J·x2≦K  equation #22

[C1 C2 C3]·x2=[C1 C2 C3]·x _(—) bpe  equation #23

Equations #24-25, shown below, define H and f.

$\begin{matrix} {H = {{\begin{bmatrix} I \\ 0 \\ 0 \end{bmatrix} \cdot Q \cdot \left\lbrack {I\mspace{14mu} 0\mspace{14mu} 0} \right\rbrack} + R}} & {{equation}\mspace{14mu} \# \; 24} \\ {f = {\begin{bmatrix} I \\ 0 \\ 0 \end{bmatrix} \cdot Q \cdot \left\lbrack {I\mspace{14mu} 0\mspace{14mu} 0} \right\rbrack \cdot {x\_ bpe}}} & {{equation}\mspace{14mu} \# \; 25} \end{matrix}$

Two possible strategies for solving QP problems are the active set method and the interior point method. In the active set method, a set of active limits is explicitly determined. In the interior point method, the active set is only implicitly determined. A limit can be inferred to be active when an associated “adjoint” variable or “slack” variable crosses certain thresholds. To improve understanding, all strategies are said to determine an active set here, although those skilled in the art would know that some strategies only implicitly do so.

The BPE may be set up so that x2=x_bpe when no limits are active. Those skilled in control theory, linear algebra, and optimization would know how to configure the control system and the weights to make this so.

Those so skilled will also known that when a limit is active, the solution of the QP problem will relax a yr degree of freedom, allowing it to differ from the corresponding yref degree of freedom. If two limits are active, two degrees of freedom will be relaxed, and so on. In general, all the yr values will relax away from all the corresponding yref values when a limit is reached. Goals with higher weights will usually be relaxed less, and those with lower weights will usually be relaxed more. Thus by selecting the weights in Q, the designer is setting the relative priorities of the goals. The mathematics of optimization shows that when viewed from an abstract coordinate system, rotated with respect to the natural one, a single goal degree of freedom is relaxed for each active limit. This goal degree of freedom will involve each of the goals to some degree, depending on the model matrices CE and J and on the weighting matrices Q and R.

Referring to FIG. 4, a first module 90 receives the desired change in basepoint values 66 (“x_bpe”), mathematical model matrices 76 (“CE”) and 78 (“J”) of sensitivity values, and processed limits 80 (“K”), and calculates an initial active set of limits 92 a (“ia_(initial)”) and a slack variable 94 (“t_feas”) in response to those inputs according to equation #24 shown below (using the notation of the common Matlab® program).

[ia,t _(—) feas]=min(0,K−[0 0 1]*x _(—) bpe)  equation #26

In the art of constrained optimization, t_feas is referred to as a slack variable. The use of t_feas in the basepoint solver 74 is consistent with the “Big K” and “Big M” techniques. The “Big K” and “Big M” refer to large weights on a slack variable

From the initial active set of limits 92 a, a goal-weighting module 95 selectively determines goal weighting matrix 96 which includes a relative priority for each of the plurality of goals 12, such that the goals 12 may be prioritized according to a relative goal prioritization scheme. In the relative goal prioritization scheme, each goal is assigned a priority or “weight” indicating its importance, and the goals are permitted to deviate from their corresponding desired goal values. The goal-weighting module 95 also determines weighting matrix 97 for the actual changes in basepoint values 70.

The second module 98 receives the weighting matrices 96 and 97, active limits 92 b, processed limits 80 (“K”), and matrices 76 (“CE”) and 78 (“J”) as inputs and calculates the actual change in basepoint values 70 a (“x2_(i)”) and an updated slack variable 100 a (“t2_(i)”) in response to the inputs, assuming the active limits 92 b should be active. However, this assumption that the limits are active may not always be true. By setting t2_(i)=0, the second module 98 has implicitly determined the values of x2_(i) and t2_(i) in the limit as the Big K and Big M weights go to infinity. This is known as the infinity K approach.

The next steps are to determine if any presumed active limits should be dropped from the active set, and if any limits implicitly presumed to be inactive should be added. The basepoint solver 74 determines if any active limits should be dropped from the active set 92 (step 106). A ratio 104 (“λ”) of the change in PI to a change in a limited variable i is used to determine if any limits can be dropped (step 106), and an updated active set 92 c (“ia”) is prepared.

A scale back operation 108 performs two steps. First, the scale back operation 108 determines if a limit should be added to the active set, as limits may be exceeded if incorrectly omitted from the active set. Second, values of actual change in basepoint values 70 a (“x2_(i)”) and the updated slack variable 100 a (“t2_(i)”) may be scaled back (step 108) if necessary to produce scaled back basepoint values 70 c (“x2”) and a scaled back slack variable 100 c (“t2”) to avoid exceeding limits that are not assumed to be active (i.e. not included in is 92 b). The scale back step 108 may use initial values of x_bpe and t_feas (for a first iteration), or may use last pass values of 100 c (“t2”) and 70 c (“x2”) for subsequent iterations, to form 100 b (“t2_(f)”) and 70 b (“x2_(f)”). Basepoint value 70 may be scaled back toward t2_(f) and x2_(f) until all limits are satisfied. The most binding limit could then be added to the active set. The basepoint solver 74 is thus able to iterate (step 109) and iteratively solve for the active set 92 b (“ia”). If no limits need to be added or dropped, the search for the active set is complete, and the value of x2 meets all limits and is output back to the greater basepoint estimator 16. A description of one example of quadratic programming, the Big K and Big M techniques, and scale back operation can be found in “Numerical Optimization” by J. Nocedal and S. Wright (Springer-Verlag 1999).

The following is one example of a basepoint estimator application. One may partition y into two parts: yg for feedback signals to improve goal response, and yc to improve limit accuracy. One may then similarly partition yb into ygb and ycb. The basepoint estimator 16 may then be applied to a system with 6 states, 4 actuator signals, and 4 goal outputs and 30 limits. The set {xb,ub,ygb, ycb} will then include 44 values. Of the 6 states, 2 may be states of the controlled system 32 (or “plant states”) and 4 may be actuator states. Of the 4 goal feedback signals, ygfb, 2 may be from sensors measuring plant variables, and 2 may be simply basepoints of two of the actuators. The latter two signals are from within the controller.

In this application, there are four reference values: two may be associated with two plant feedback measurements and two may be associated with the two actuator basepoints included in yg. All 30 limits may have elements in feedback vector yc: 22 are from feedback sensors on the plant and 8 are from the actuator model implemented in the controller. Of the 30 limits, 4 may be maximums and minimums of the two plant state values, 8 may be maximums and minimums of the four actuator output values, and 18 may be otherwise. In this application example, there are 18 dependencies between the 44 basepoint values: 4 of xb that may be bases for actuator states are equal to the 4 actuator base values in ub, 2 in ygb that may be actuator bases are equal to the 2 associated actuator bases in ub, 2 in ygb that may be state bases are equal to the associated 2 state base values in xb, 4 elements in ycb may be plus and minus the bases for two plant states and may be equal to the associated values in ±xb, and 8 in ycb may be plus and minus bases for 4 actuators and may be equal to the associated ±ub values.

The 18 dependencies reduce the 44 basepoints to 26 that are independent values to be estimated. These 26 may be grouped into ycb, bpf, and yr as the following: ycb may include bases for 18 of the limits that are other than states and actuators, yr may include 4 bases for 2 of the 4 plant variables measured by feedback sensors and 2 actuator base values, and bpf may include 4 bases including 2 plant states and the 2 actuator bases not included in yr.

Although a preferred embodiment of this invention has been disclosed, a worker of ordinary skill in this art would recognize that certain modifications would come within the scope of this invention. For that reason, the following claims should be studied to determine the true scope and content of this invention. 

1. A method of estimating a basepoint, comprising: receiving a plurality of goals, wherein each goal has a desired value; receiving a plurality of sensor feedback signals from a controlled system; receiving a plurality of predicted output values of the controlled system from a mathematical model; estimating a desired change for a plurality of basepoint values in response to the goals, the feedback, and the predicted output values; calculating an actual change in basepoint values in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits; and combining the actual change in basepoint values with last pass values of the plurality of basepoint values to produce an updated basepoint estimate.
 2. The method of claim 1, wherein said step of calculating an actual change in basepoint values in response to a plurality of limits and the desired change for the plurality of basepoint values according to a plurality of goal weights while holding limits includes: receiving the plurality of limits; receiving a plurality of matrices from the mathematical model, wherein one of the matrices is a goal-weighting matrix that includes the plurality of goal weights; receiving the plurality of desired changes for the plurality of basepoint values; formulating a constrained optimization problem that minimizes a difference between the actual changes in basepoint values and the desired change in basepoint values, and that ensures all limits are met; and solving the constrained optimization problem to determine updated actual changes in basepoint values in response to the desired changes, the goal-weighting matrices, and the limits.
 3. The method of claim 2, further comprising: defining the goal weighting matrix to include a relative priority for each of the plurality of goals; and defining a basepoint weighting matrix for the actual changes in basepoint values.
 4. The method of claim 3, wherein said step of formulating a constrained optimization problem includes: defining a performance index to include linear terms and quadratic terms; and defining a linear approximation of the mathematical model to relate the actual changes in basepoint values to a plurality of limited variables.
 5. The method of claim 2, wherein said step of solving the constrained optimization problem to determine updated actual changes in basepoint values is implemented using a quadratic programming algorithm.
 6. The method of claim 5, wherein the quadratic programming algorithm includes at least one of a Big M technique or a Big K technique.
 7. The method of claim 6, wherein said at least one of the Big M technique or the Big K technique is modified to be a infinite K technique.
 8. The method of claim 1, wherein a higher goal weight corresponds to a higher priority.
 9. The method of claim 1, wherein said step of calculating an actual change in basepoint values maintains feedback dynamic properties of an estimator loop.
 10. The method of claim 1, wherein said step of receiving a plurality of predicted output values includes: creating a mathematical model to predict behavior of the controlled system; and mapping the actual change in basepoint values into a format that may be processed by the mathematical model.
 11. The method of claim 1, wherein the mathematical model is a time varying linear model.
 12. The method of claim 11, wherein the mathematical model is a quasi-linear parameter varying model.
 13. The method of claim 2, further comprising: using the matrices from the mathematical model to maintain desirable dynamics throughout a controlled system operating envelope and across various permutations of active limits.
 14. The method of claim 1, further comprising: using a first gain matrix to provide a smooth transition off of a limit.
 15. The method of claim 14, further comprising: using a second gain matrix to provide a smooth transition onto a limit.
 16. The method of claim 1, further comprising: creating actuator requests for the controlled system in response to the updated basepoint estimate. 